An algorithm for recognising walkers

Abstract

In this paper, we present an algorithm to recognise walking people, based upon extracting the spatio-temporal trajectories of the joints of a walking subject.

Subjects are filmed with LEDs attached to their joints and head such that the lights are the only objects visible in the film sequence — a method known as moving light displays (MLDs). Lights are tracked through the sequence of frames and are labelled based on human walking behaviour. In the case of self-occluded lights, a radial basis function neural network was trained and used for predicting the positions of occluded markers. The trajectory of each MLD is transformed using a 2D fast Fourier transform. Components of the FFT for all MLDs are considered as the feature vector of each subject. This is fed to a multi-layer perceptron (MLP) for classification.

The algorithm was used to recognise four subjects — 3 males and 1 female. For each subject, 10 gait cycles were used for training and 5 for testing the MLP. Backpropagation was used to train the network. Results show that the algorithm is a promising technique for recognising subjects by their gait.